The present invention relates in general to monitoring vehicular traffic on roadways, and, more specifically, to utilizing automatic location and velocity information provided by mobile communication devices (e.g., cellular phones, PDA""s, and laptops communicating via CDMA, CPDP, GSM/GPRS, 802.11 expansion cards) to detect traffic congestion.
Many different techniques have been investigated for monitoring vehicular traffic flows in order to identify areas of congestion or lane blockages so that other traffic can be re-routed away from the problem. The monitoring devices in typical prior art systems (such as cameras, radar sensors, magnetic sensors, and weight sensors) have been deployed in or around the roadway in order to detect passing cars and trucks. With the sensors being fixed in place, coverage is limited to the areas where the sensors have been installed. In a large area (such as a metropolitan area), the large number of sensors that is required would result in high cost. In addition, many traffic problems do not occur at these fixed locations but instead occur at locations not covered by a sensor. Furthermore, a communication system and a data processing system must be provided in order to consolidate the sensor data for analysis, which is also very expensive.
It is known to process traffic data using statistical methods to characterize a traffic flow. Such methods, however, can tie up an excessive amount of computational resources and/or often depend on significant human intervention, To reduce the cost and increase the reliability of a traffic monitoring system, it would be desirable to avoid excess computations and human intervention.
With the proliferation of mobile communication devices (e.g., cellular telephones, PDA""s, laptops, etc . . . ) use in vehicles, an opportunity has been seen to utilize the mobile communication devices or the carrier""s wireless communication system itself for providing position sensors to monitor vehicle movement. Particularly in the United States, automatic location identification (ALI) capability within a cellular telephone system is being mandated by law so that the geographic position of a caller to emergency services is instantly transmitted to the emergency service provider (referred to as enhanced 911 services). A caller""s position can be determined by providing the location finding capability in the cellular network (e.g., by triangulation), using a location capability in the mobile communication devices, or both working together.
A solution being widely adopted employs global position system (GPS) technology with a GPS receiver being built into each cellular phone. Along with geographic position coordinates (i.e., longitude and latitude), a GPS receiver typically determines the instantaneous velocity (i.e., speed and heading) at which the receiver is moving. When the cellular phone is carried in a motor vehicle, the position and velocity information detected with the GPS receiver can be used to identify traffic conditions of the roadway on which the vehicle is traveling.
In order to provide accurate and reliable characterization of traffic conditions, it is necessary to obtain a sufficient population of data samples (i.e., proportion of sampled vehicles to total vehicles). As the mandate for position-enabled cellular phone service ramps up, a critical mass will be reached so that location and velocity data from phones will be sufficient to characterize traffic conditions.
Statistical analysis of vehicle data intended to identify trends in traffic volume, or flow, usually consume large amounts of computational resources. It is important to quickly detect the occurrence of an accident or other road blockage, as well as the clearance of the accident or re-opening of the road in order to take effective traffic management actions. Yet, it is equally important to avoid any false detections from the anomalous behavior of a small number of vehicles or from data errors (e.g., a car or two pulling off of the road to change drivers). Consequently, data must be aggregated and a certain amount of data processing cannot be avoided (such as, data filtering of spurious data, aggregation by road segment, and aggregation by direction of travel). As large amounts of data are collected, efficient methods are needed for automatically analyzing the data to detect the road conditions of interest.
The present invention provides the advantages of efficient sorting of incoming location and velocity data from mobile telephones, efficient use of processing resources, and a fast response time to changes in traffic conditions.
In one aspect of the invention, a vehicular traffic monitoring system communicates with a plurality of mobile communication devices carried in moving vehicles and capable of determining their respective geographic positions and velocities. A data collector receives data samples from the plurality of mobile communication devices, each data sample comprising instantaneous location and velocity information of a respective mobile device at a respective time. A road segment identifier is coupled to the data collector for finding matching data samples wherein a respective instantaneous location corresponds to one of a plurality of road segments monitored by the traffic monitoring system. A sliding average calculator coupled to the road segment identifier determines an average speed corresponding to matched data samples for a particular one of the road segments in response to a predetermined sliding window. A road segment is comprised of a portion of a roadway with all lanes moving in the same direction. A state change detector coupled to the sliding average calculator detects a traffic state change at the road segment in response to the average speed in the predetermined sliding window determined at first and second times. A congestion alerting mechanism coupled to the state change detector routes either a congesting/congested state or a clearing/clear state notification for the respective road segment in response to the detected traffic state change. The state change detector may preferably detect a rapid variance in the average speed.